Sui binari variabili del lessico ferroviario italiano dell'Otto e Novecento
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Riassunto : In questo articolo si esamina l’ingresso del lessico ferroviario nei vocabolari italiani dell’Ottocento e di inizio Novecento, osservando il lento accoglimento di parole che si stavano allora diffondendo nell’uso e nella vita quotidiana di persone comuni, anche attraverso le pagine dei giornali, a dispetto delle indicazioni e di alcuni tentativi di respingimento operati da lessicografi. Per tracciare questa storia di termini concorrenti, alcuni dei quali sono oggi diventati “parole scomparse”, vengono consultati principalmente dizionari di neologismi e dizionari dell’uso. Tra le parole di ambito ferroviario qui prese in esame abbiamo ferrovia, deragliare, locomotiva, mastodonte, rotaia, traforo, tunnel, viadotto, cremagliera, espresso, direttissimo, rapido, nave-traghetto, ferry-boat, vagone-letto, sleeping car, ferroviere, casellante, vettura, vagone, carrozza, treno blindato.||Abstract : The article is about the railway words in the Italian dictionaries of the XIX and early XX centuries. Few words of this technical language were in the list of headwords of the Italian general dictionaries, even if new words of modern means of transport were spreading in daily life and through newspapers. Lexicographers tried to regulate them and to give rules, not always successfully. Sources of this research on railway words (some of which have now become “disappeared words”) are dictionaries of neologisms and general dictionaries. Among the words examined in the article, we mention: ferrovia, deragliare, locomotiva, mastodonte, rotaia, traforo, tunnel, viadotto, cremagliera, espresso, direttissimo, rapido, nave-traghetto, ferry-boat, vagone-letto, sleeping car, ferroviere, casellante, vettura, vagone, carrozza, treno blindato.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it